Target Driven Adaptive Loss For Infrared Small Target Detection
This work addresses incremental improvements in infrared small target detection for applications like surveillance or remote sensing.
The paper tackles the problem of infrared small target detection by proposing a target driven adaptive loss to improve detection in local regions and robustness to small scale and low contrast, achieving better performance than existing losses on three datasets.
We propose a target driven adaptive (TDA) loss to enhance the performance of infrared small target detection (IRSTD). Prior works have used loss functions, such as binary cross-entropy loss and IoU loss, to train segmentation models for IRSTD. Minimizing these loss functions guides models to extract pixel-level features or global image context. However, they have two issues: improving detection performance for local regions around the targets and enhancing robustness to small scale and low local contrast. To address these issues, the proposed TDA loss introduces a patch-based mechanism, and an adaptive adjustment strategy to scale and local contrast. The proposed TDA loss leads the model to focus on local regions around the targets and pay particular attention to targets with smaller scales and lower local contrast. We evaluate the proposed method on three datasets for IRSTD. The results demonstrate that the proposed TDA loss achieves better detection performance than existing losses on these datasets.